Overview

Dataset statistics

Number of variables13
Number of observations2391
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory380.7 KiB
Average record size in memory163.1 B

Variable types

NUM12
CAT1

Warnings

AllCallsUnder4min is highly correlated with EMSResponseUnder4minHigh correlation
EMSResponseUnder4min is highly correlated with AllCallsUnder4minHigh correlation
Alarm Date has unique values Unique
Fire_Count has 564 (23.6%) zeros Zeros
FireResponseUnder4min has 902 (37.7%) zeros Zeros
FireResponseUnder4minPercent has 902 (37.7%) zeros Zeros

Reproduction

Analysis started2020-12-12 23:37:29.170769
Analysis finished2020-12-12 23:37:40.724210
Duration11.55 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Alarm Date
Categorical

UNIQUE

Distinct2391
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.8 KiB
09/16/2013
 
1
12/28/2012
 
1
06/23/2015
 
1
10/29/2016
 
1
08/30/2013
 
1
Other values (2386)
2386 
ValueCountFrequency (%) 
09/16/20131< 0.1%
 
12/28/20121< 0.1%
 
06/23/20151< 0.1%
 
10/29/20161< 0.1%
 
08/30/20131< 0.1%
 
03/18/20141< 0.1%
 
11/29/20161< 0.1%
 
05/04/20141< 0.1%
 
03/28/20181< 0.1%
 
08/12/20151< 0.1%
 
12/19/20171< 0.1%
 
08/19/20121< 0.1%
 
05/10/20151< 0.1%
 
12/20/20171< 0.1%
 
03/30/20131< 0.1%
 
07/04/20141< 0.1%
 
02/14/20121< 0.1%
 
03/10/20181< 0.1%
 
09/24/20151< 0.1%
 
09/01/20131< 0.1%
 
09/16/20151< 0.1%
 
01/18/20161< 0.1%
 
01/16/20141< 0.1%
 
12/28/20171< 0.1%
 
04/22/20161< 0.1%
 
Other values (2366)236699.0%
 
2020-12-12T18:37:40.802279image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2391 ?
Unique (%)100.0%
2020-12-12T18:37:40.879345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0535522.4%
 
/478220.0%
 
1441018.4%
 
2415217.4%
 
39343.9%
 
58183.4%
 
68123.4%
 
48113.4%
 
78053.4%
 
86212.6%
 
94101.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1912880.0%
 
Other Punctuation478220.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0535528.0%
 
1441023.1%
 
2415221.7%
 
39344.9%
 
58184.3%
 
68124.2%
 
48114.2%
 
78054.2%
 
86213.2%
 
94102.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/4782100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common23910100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0535522.4%
 
/478220.0%
 
1441018.4%
 
2415217.4%
 
39343.9%
 
58183.4%
 
68123.4%
 
48113.4%
 
78053.4%
 
86212.6%
 
94101.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII23910100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0535522.4%
 
/478220.0%
 
1441018.4%
 
2415217.4%
 
39343.9%
 
58183.4%
 
68123.4%
 
48113.4%
 
78053.4%
 
86212.6%
 
94101.7%
 

Total Calls
Real number (ℝ≥0)

Distinct85
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.67837725
Minimum35
Maximum154
Zeros0
Zeros (%)0.0%
Memory size18.8 KiB
2020-12-12T18:37:40.950906image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile48
Q159
median67
Q378
95-th percentile93
Maximum154
Range119
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.01627375
Coefficient of variation (CV)0.2040856862
Kurtosis0.8023960406
Mean68.67837725
Median Absolute Deviation (MAD)9
Skewness0.5944399929
Sum164210
Variance196.4559298
MonotocityNot monotonic
2020-12-12T18:37:41.035980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
62803.3%
 
64763.2%
 
60763.2%
 
66733.1%
 
61723.0%
 
65723.0%
 
67713.0%
 
69702.9%
 
59662.8%
 
68642.7%
 
71622.6%
 
56612.6%
 
73602.5%
 
54592.5%
 
63582.4%
 
55582.4%
 
72562.3%
 
79542.3%
 
74532.2%
 
77522.2%
 
57522.2%
 
58492.0%
 
76492.0%
 
52461.9%
 
70461.9%
 
Other values (60)85635.8%
 
ValueCountFrequency (%) 
3520.1%
 
3620.1%
 
3730.1%
 
3920.1%
 
4080.3%
 
4160.3%
 
4290.4%
 
4370.3%
 
44130.5%
 
45150.6%
 
ValueCountFrequency (%) 
1541< 0.1%
 
1261< 0.1%
 
1251< 0.1%
 
1231< 0.1%
 
11820.1%
 
1171< 0.1%
 
11620.1%
 
11430.1%
 
11230.1%
 
11130.1%
 

Fire_Count
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.757841907
Minimum0
Maximum15
Zeros564
Zeros (%)23.6%
Memory size18.8 KiB
2020-12-12T18:37:41.110544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile5
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.666905341
Coefficient of variation (CV)0.9482680634
Kurtosis5.031737514
Mean1.757841907
Median Absolute Deviation (MAD)1
Skewness1.622135073
Sum4203
Variance2.778573416
MonotocityNot monotonic
2020-12-12T18:37:41.172597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
169028.9%
 
056423.6%
 
251721.6%
 
330712.8%
 
41727.2%
 
5672.8%
 
6321.3%
 
7190.8%
 
8130.5%
 
940.2%
 
1030.1%
 
151< 0.1%
 
111< 0.1%
 
141< 0.1%
 
ValueCountFrequency (%) 
056423.6%
 
169028.9%
 
251721.6%
 
330712.8%
 
41727.2%
 
5672.8%
 
6321.3%
 
7190.8%
 
8130.5%
 
940.2%
 
ValueCountFrequency (%) 
151< 0.1%
 
141< 0.1%
 
111< 0.1%
 
1030.1%
 
940.2%
 
8130.5%
 
7190.8%
 
6321.3%
 
5672.8%
 
41727.2%
 

EMS_Count
Real number (ℝ≥0)

Distinct64
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.72354663
Minimum21
Maximum85
Zeros0
Zeros (%)0.0%
Memory size18.8 KiB
2020-12-12T18:37:41.250164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile33.5
Q141
median48
Q355
95-th percentile67
Maximum85
Range64
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.23077367
Coefficient of variation (CV)0.2099759639
Kurtosis-0.003072508756
Mean48.72354663
Median Absolute Deviation (MAD)7
Skewness0.3608575704
Sum116498
Variance104.6687298
MonotocityNot monotonic
2020-12-12T18:37:41.332735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
451054.4%
 
441034.3%
 
40984.1%
 
50964.0%
 
48954.0%
 
42933.9%
 
46933.9%
 
47903.8%
 
54813.4%
 
39803.3%
 
53793.3%
 
43783.3%
 
41763.2%
 
51733.1%
 
49723.0%
 
56702.9%
 
52672.8%
 
55652.7%
 
38572.4%
 
58552.3%
 
59512.1%
 
37502.1%
 
57492.0%
 
35472.0%
 
61472.0%
 
Other values (39)52121.8%
 
ValueCountFrequency (%) 
2120.1%
 
2230.1%
 
231< 0.1%
 
2420.1%
 
2520.1%
 
2680.3%
 
2770.3%
 
2890.4%
 
2990.4%
 
30100.4%
 
ValueCountFrequency (%) 
851< 0.1%
 
841< 0.1%
 
8320.1%
 
8220.1%
 
811< 0.1%
 
801< 0.1%
 
791< 0.1%
 
7850.2%
 
7750.2%
 
7530.1%
 

Other_Count
Real number (ℝ≥0)

Distinct53
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.195734
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Memory size18.8 KiB
2020-12-12T18:37:41.421311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q114
median17
Q321
95-th percentile29
Maximum100
Range99
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.853041852
Coefficient of variation (CV)0.3766290413
Kurtosis14.4008765
Mean18.195734
Median Absolute Deviation (MAD)4
Skewness2.144269731
Sum43506
Variance46.96418263
MonotocityNot monotonic
2020-12-12T18:37:41.508386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
181747.3%
 
161677.0%
 
141606.7%
 
151536.4%
 
201526.4%
 
191496.2%
 
171476.1%
 
131375.7%
 
211305.4%
 
121184.9%
 
111154.8%
 
22873.6%
 
23813.4%
 
10743.1%
 
24672.8%
 
26672.8%
 
25672.8%
 
9592.5%
 
27461.9%
 
28351.5%
 
8311.3%
 
30220.9%
 
29220.9%
 
32190.8%
 
7180.8%
 
Other values (28)943.9%
 
ValueCountFrequency (%) 
11< 0.1%
 
41< 0.1%
 
51< 0.1%
 
6170.7%
 
7180.8%
 
8311.3%
 
9592.5%
 
10743.1%
 
111154.8%
 
121184.9%
 
ValueCountFrequency (%) 
1001< 0.1%
 
761< 0.1%
 
661< 0.1%
 
631< 0.1%
 
591< 0.1%
 
5620.1%
 
531< 0.1%
 
5220.1%
 
5120.1%
 
4920.1%
 

FireResponseUnder4min
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.107904642
Minimum0
Maximum10
Zeros902
Zeros (%)37.7%
Memory size18.8 KiB
2020-12-12T18:37:41.581449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.203231458
Coefficient of variation (CV)1.086042437
Kurtosis3.490975994
Mean1.107904642
Median Absolute Deviation (MAD)1
Skewness1.463714558
Sum2649
Variance1.447765942
MonotocityNot monotonic
2020-12-12T18:37:41.639999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
090237.7%
 
177532.4%
 
243018.0%
 
31847.7%
 
4622.6%
 
5251.0%
 
670.3%
 
730.1%
 
820.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
090237.7%
 
177532.4%
 
243018.0%
 
31847.7%
 
4622.6%
 
5251.0%
 
670.3%
 
730.1%
 
820.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
101< 0.1%
 
820.1%
 
730.1%
 
670.3%
 
5251.0%
 
4622.6%
 
31847.7%
 
243018.0%
 
177532.4%
 
090237.7%
 

EMSResponseUnder4min
Real number (ℝ≥0)

HIGH CORRELATION

Distinct54
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.56712673
Minimum7
Maximum68
Zeros0
Zeros (%)0.0%
Memory size18.8 KiB
2020-12-12T18:37:41.711061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile19
Q125
median31
Q337
95-th percentile47
Maximum68
Range61
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.596218303
Coefficient of variation (CV)0.2723155128
Kurtosis0.2533618732
Mean31.56712673
Median Absolute Deviation (MAD)6
Skewness0.4689070682
Sum75477
Variance73.8949691
MonotocityNot monotonic
2020-12-12T18:37:41.788627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
271245.2%
 
311205.0%
 
281184.9%
 
301154.8%
 
291114.6%
 
321094.6%
 
331094.6%
 
241064.4%
 
351004.2%
 
26943.9%
 
25923.8%
 
34873.6%
 
22853.6%
 
23803.3%
 
37803.3%
 
41723.0%
 
38662.8%
 
36652.7%
 
39622.6%
 
40542.3%
 
21512.1%
 
42472.0%
 
20441.8%
 
19371.5%
 
43361.5%
 
Other values (29)32713.7%
 
ValueCountFrequency (%) 
71< 0.1%
 
81< 0.1%
 
1160.3%
 
1230.1%
 
1380.3%
 
1420.1%
 
15190.8%
 
16180.8%
 
17210.9%
 
18281.2%
 
ValueCountFrequency (%) 
681< 0.1%
 
671< 0.1%
 
621< 0.1%
 
611< 0.1%
 
5920.1%
 
5820.1%
 
5750.2%
 
5650.2%
 
54130.5%
 
5390.4%
 

OtherResponseUnder4min
Real number (ℝ≥0)

Distinct30
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.77457131
Minimum0
Maximum55
Zeros2
Zeros (%)0.1%
Memory size18.8 KiB
2020-12-12T18:37:41.865193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median10
Q313
95-th percentile19
Maximum55
Range55
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.502602406
Coefficient of variation (CV)0.4178915594
Kurtosis4.12159444
Mean10.77457131
Median Absolute Deviation (MAD)3
Skewness1.010663545
Sum25762
Variance20.27342842
MonotocityNot monotonic
2020-12-12T18:37:41.929749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
112249.4%
 
82229.3%
 
92189.1%
 
102169.0%
 
72108.8%
 
121948.1%
 
61596.6%
 
131496.2%
 
141446.0%
 
151044.3%
 
5984.1%
 
16803.3%
 
4733.1%
 
17542.3%
 
18542.3%
 
19421.8%
 
20301.3%
 
3271.1%
 
21220.9%
 
22180.8%
 
2150.6%
 
23130.5%
 
2570.3%
 
2460.3%
 
140.2%
 
Other values (5)80.3%
 
ValueCountFrequency (%) 
020.1%
 
140.2%
 
2150.6%
 
3271.1%
 
4733.1%
 
5984.1%
 
61596.6%
 
72108.8%
 
82229.3%
 
92189.1%
 
ValueCountFrequency (%) 
551< 0.1%
 
321< 0.1%
 
2730.1%
 
261< 0.1%
 
2570.3%
 
2460.3%
 
23130.5%
 
22180.8%
 
21220.9%
 
20301.3%
 

AllCallsUnder4min
Real number (ℝ≥0)

HIGH CORRELATION

Distinct69
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.45043915
Minimum9
Maximum93
Zeros0
Zeros (%)0.0%
Memory size18.8 KiB
2020-12-12T18:37:42.006315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile27.5
Q136
median42
Q350
95-th percentile64
Maximum93
Range84
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.06681641
Coefficient of variation (CV)0.2546997598
Kurtosis0.4871342094
Mean43.45043915
Median Absolute Deviation (MAD)7
Skewness0.590323627
Sum103890
Variance122.4744255
MonotocityNot monotonic
2020-12-12T18:37:42.084882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
37974.1%
 
35954.0%
 
38923.8%
 
40923.8%
 
39923.8%
 
44913.8%
 
41893.7%
 
42883.7%
 
45863.6%
 
46853.6%
 
36843.5%
 
43833.5%
 
34763.2%
 
33682.8%
 
49682.8%
 
48682.8%
 
47662.8%
 
50622.6%
 
32542.3%
 
53522.2%
 
30512.1%
 
31502.1%
 
51472.0%
 
54441.8%
 
57431.8%
 
Other values (44)56823.8%
 
ValueCountFrequency (%) 
91< 0.1%
 
151< 0.1%
 
181< 0.1%
 
1950.2%
 
2070.3%
 
2140.2%
 
22130.5%
 
23100.4%
 
24130.5%
 
25170.7%
 
ValueCountFrequency (%) 
931< 0.1%
 
911< 0.1%
 
901< 0.1%
 
831< 0.1%
 
8020.1%
 
791< 0.1%
 
7840.2%
 
7720.1%
 
7650.2%
 
751< 0.1%
 

FireResponseUnder4minPercent
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.18695107
Minimum0
Maximum100
Zeros902
Zeros (%)37.7%
Memory size18.8 KiB
2020-12-12T18:37:42.160948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median50
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation43.12985857
Coefficient of variation (CV)0.8768557032
Kurtosis-1.697145757
Mean49.18695107
Median Absolute Deviation (MAD)50
Skewness0.003197124039
Sum117606
Variance1860.1847
MonotocityNot monotonic
2020-12-12T18:37:42.230508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%) 
090237.7%
 
10080933.8%
 
5027711.6%
 
671395.8%
 
33713.0%
 
75662.8%
 
25261.1%
 
60180.8%
 
80170.7%
 
40160.7%
 
2080.3%
 
7180.3%
 
5750.2%
 
6240.2%
 
1740.2%
 
8340.2%
 
1220.1%
 
8820.1%
 
8620.1%
 
2920.1%
 
4320.1%
 
701< 0.1%
 
271< 0.1%
 
441< 0.1%
 
381< 0.1%
 
Other values (3)30.1%
 
ValueCountFrequency (%) 
090237.7%
 
1220.1%
 
1740.2%
 
2080.3%
 
221< 0.1%
 
25261.1%
 
271< 0.1%
 
2920.1%
 
33713.0%
 
381< 0.1%
 
ValueCountFrequency (%) 
10080933.8%
 
891< 0.1%
 
8820.1%
 
8620.1%
 
8340.2%
 
80170.7%
 
75662.8%
 
7180.3%
 
701< 0.1%
 
671395.8%
 

EMSResponseUnder4minPercent
Real number (ℝ≥0)

Distinct62
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.53910498
Minimum13
Maximum90
Zeros0
Zeros (%)0.0%
Memory size18.8 KiB
2020-12-12T18:37:42.307073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile48
Q159
median65
Q371
95-th percentile79
Maximum90
Range77
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.423288468
Coefficient of variation (CV)0.1460089735
Kurtosis0.8059335879
Mean64.53910498
Median Absolute Deviation (MAD)6
Skewness-0.4552762755
Sum154313
Variance88.79836556
MonotocityNot monotonic
2020-12-12T18:37:42.389644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
651255.2%
 
621164.9%
 
671124.7%
 
641074.5%
 
681064.4%
 
611024.3%
 
691014.2%
 
70954.0%
 
66933.9%
 
71833.5%
 
60823.4%
 
59753.1%
 
63733.1%
 
57733.1%
 
74702.9%
 
72692.9%
 
73662.8%
 
76662.8%
 
58642.7%
 
56562.3%
 
75522.2%
 
55502.1%
 
53461.9%
 
54441.8%
 
78431.8%
 
Other values (37)42217.6%
 
ValueCountFrequency (%) 
131< 0.1%
 
211< 0.1%
 
231< 0.1%
 
251< 0.1%
 
2820.1%
 
291< 0.1%
 
311< 0.1%
 
341< 0.1%
 
3530.1%
 
3640.2%
 
ValueCountFrequency (%) 
901< 0.1%
 
8930.1%
 
8850.2%
 
8630.1%
 
8570.3%
 
8470.3%
 
83100.4%
 
82200.8%
 
81140.6%
 
80341.4%
 

OtherResponseUnder4minPercent
Real number (ℝ≥0)

Distinct85
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.47093266
Minimum0
Maximum100
Zeros2
Zeros (%)0.1%
Memory size18.8 KiB
2020-12-12T18:37:42.479222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q150
median60
Q369
95-th percentile82
Maximum100
Range100
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.24078253
Coefficient of variation (CV)0.2394578643
Kurtosis0.2154989153
Mean59.47093266
Median Absolute Deviation (MAD)10
Skewness-0.2514220389
Sum142195
Variance202.799887
MonotocityNot monotonic
2020-12-12T18:37:42.565796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
671405.9%
 
501335.6%
 
62954.0%
 
56863.6%
 
57813.4%
 
64803.3%
 
60723.0%
 
55723.0%
 
58672.8%
 
65672.8%
 
53662.8%
 
71662.8%
 
69652.7%
 
75642.7%
 
70592.5%
 
44592.5%
 
73582.4%
 
47522.2%
 
54512.1%
 
61502.1%
 
59411.7%
 
52411.7%
 
63391.6%
 
40381.6%
 
68371.5%
 
Other values (60)71229.8%
 
ValueCountFrequency (%) 
020.1%
 
81< 0.1%
 
101< 0.1%
 
111< 0.1%
 
131< 0.1%
 
1420.1%
 
151< 0.1%
 
171< 0.1%
 
1820.1%
 
191< 0.1%
 
ValueCountFrequency (%) 
10070.3%
 
951< 0.1%
 
941< 0.1%
 
931< 0.1%
 
9270.3%
 
9130.1%
 
9080.3%
 
8990.4%
 
8880.3%
 
8780.3%
 

AllCallsUnder4minPercent
Real number (ℝ≥0)

Distinct59
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.1384358
Minimum18
Maximum89
Zeros0
Zeros (%)0.0%
Memory size18.8 KiB
2020-12-12T18:37:42.650869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile48
Q158
median64
Q369
95-th percentile76
Maximum89
Range71
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.479736559
Coefficient of variation (CV)0.1343038745
Kurtosis1.048705914
Mean63.1384358
Median Absolute Deviation (MAD)5
Skewness-0.4876435979
Sum150964
Variance71.90593211
MonotocityNot monotonic
2020-12-12T18:37:42.733440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
651325.5%
 
621295.4%
 
671265.3%
 
641174.9%
 
681124.7%
 
661104.6%
 
591034.3%
 
611024.3%
 
631004.2%
 
60954.0%
 
70943.9%
 
58923.8%
 
69903.8%
 
56783.3%
 
71773.2%
 
57753.1%
 
72652.7%
 
55652.7%
 
54532.2%
 
74492.0%
 
73492.0%
 
75441.8%
 
77391.6%
 
52391.6%
 
53351.5%
 
Other values (34)32113.4%
 
ValueCountFrequency (%) 
181< 0.1%
 
211< 0.1%
 
251< 0.1%
 
261< 0.1%
 
271< 0.1%
 
301< 0.1%
 
311< 0.1%
 
3220.1%
 
351< 0.1%
 
361< 0.1%
 
ValueCountFrequency (%) 
891< 0.1%
 
871< 0.1%
 
8530.1%
 
8370.3%
 
8250.2%
 
8190.4%
 
80150.6%
 
79190.8%
 
78190.8%
 
77391.6%
 

Interactions

2020-12-12T18:37:29.615152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:37:29.687214image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T18:37:39.641780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:37:39.719347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:37:39.796913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:37:39.876482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:37:39.954048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T18:37:40.267318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T18:37:42.814510image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T18:37:42.961136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T18:37:43.111265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T18:37:43.258392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-12T18:37:40.427957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:37:40.598603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

Alarm DateTotal CallsFire_CountEMS_CountOther_CountFireResponseUnder4minEMSResponseUnder4minOtherResponseUnder4minAllCallsUnder4minFireResponseUnder4minPercentEMSResponseUnder4minPercentOtherResponseUnder4minPercentAllCallsUnder4minPercent
008/11/201465440214251847100628672
103/23/201369155130416470754668
212/13/201451038130236290614657
304/19/201455339130315360793865
409/23/20124623311126103750799180
501/20/20127324922124113650495049
607/13/20157925027133185250666766
709/28/201462343163251038100586261
802/27/2013421356120425100576760
908/07/20125414850332350694065

Last rows

Alarm DateTotal CallsFire_CountEMS_CountOther_CountFireResponseUnder4minEMSResponseUnder4minOtherResponseUnder4minAllCallsUnder4minFireResponseUnder4minPercentEMSResponseUnder4minPercentOtherResponseUnder4minPercentAllCallsUnder4minPercent
238107/08/2018660471903915540837982
238207/13/2018101278212621680100797679
238305/29/20188125920146115850785572
238407/15/20188526320144176250708573
238506/18/201811247731361269075798480
238607/12/20189646923249176850717471
238707/16/201887348363342360100716469
238807/18/20189136325143186233687268
238907/17/201812527152149277750695262
239007/14/20189235732238246467677570